Overview

Dataset statistics

Number of variables16
Number of observations16724
Missing cells21450
Missing cells (%)8.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.3 MiB
Average record size in memory454.6 B

Variable types

Numeric10
Categorical5
Unsupported1

Warnings

name has a high cardinality: 16347 distinct values High cardinality
host_name has a high cardinality: 5290 distinct values High cardinality
last_review has a high cardinality: 2040 distinct values High cardinality
id is highly correlated with host_idHigh correlation
host_id is highly correlated with idHigh correlation
number_of_reviews is highly correlated with reviews_per_monthHigh correlation
reviews_per_month is highly correlated with number_of_reviewsHigh correlation
number_of_reviews is highly correlated with reviews_per_monthHigh correlation
reviews_per_month is highly correlated with number_of_reviewsHigh correlation
id is highly correlated with calculated_host_listings_countHigh correlation
host_id is highly correlated with calculated_host_listings_countHigh correlation
latitude is highly correlated with calculated_host_listings_countHigh correlation
longitude is highly correlated with calculated_host_listings_countHigh correlation
price is highly correlated with calculated_host_listings_countHigh correlation
minimum_nights is highly correlated with calculated_host_listings_countHigh correlation
number_of_reviews is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
reviews_per_month is highly correlated with number_of_reviews and 1 other fieldsHigh correlation
calculated_host_listings_count is highly correlated with id and 7 other fieldsHigh correlation
longitude is highly correlated with neighbourhood and 1 other fieldsHigh correlation
id is highly correlated with host_idHigh correlation
number_of_reviews is highly correlated with reviews_per_monthHigh correlation
host_id is highly correlated with idHigh correlation
reviews_per_month is highly correlated with number_of_reviewsHigh correlation
neighbourhood is highly correlated with longitude and 1 other fieldsHigh correlation
latitude is highly correlated with longitude and 1 other fieldsHigh correlation
neighbourhood_group has 16724 (100.0%) missing values Missing
last_review has 2318 (13.9%) missing values Missing
reviews_per_month has 2318 (13.9%) missing values Missing
price is highly skewed (γ1 = 25.77570208) Skewed
minimum_nights is highly skewed (γ1 = 37.94287912) Skewed
name is uniformly distributed Uniform
id has unique values Unique
neighbourhood_group is an unsupported type, check if it needs cleaning or further analysis Unsupported
number_of_reviews has 2318 (13.9%) zeros Zeros
availability_365 has 10763 (64.4%) zeros Zeros

Reproduction

Analysis started2021-08-02 21:17:45.260106
Analysis finished2021-08-02 21:18:16.243628
Duration30.98 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct16724
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20974176.06
Minimum2818
Maximum50807501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size130.8 KiB
2021-08-02T23:18:16.313551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2818
5-th percentile1935359.35
Q110133057.75
median19193415
Q330854498.75
95-th percentile44383565.6
Maximum50807501
Range50804683
Interquartile range (IQR)20721441

Descriptive statistics

Standard deviation13275219.55
Coefficient of variation (CV)0.6329316355
Kurtosis-0.8381837558
Mean20974176.06
Median Absolute Deviation (MAD)9996165
Skewness0.3936053076
Sum3.507721204 × 1011
Variance1.762314542 × 1014
MonotonicityStrictly increasing
2021-08-02T23:18:16.577961image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28181
 
< 0.1%
258343311
 
< 0.1%
258486551
 
< 0.1%
258533451
 
< 0.1%
258564831
 
< 0.1%
258595601
 
< 0.1%
258751801
 
< 0.1%
258774711
 
< 0.1%
258777051
 
< 0.1%
258794951
 
< 0.1%
Other values (16714)16714
99.9%
ValueCountFrequency (%)
28181
< 0.1%
201681
< 0.1%
254281
< 0.1%
278861
< 0.1%
288711
< 0.1%
290511
< 0.1%
411251
< 0.1%
431091
< 0.1%
439801
< 0.1%
463861
< 0.1%
ValueCountFrequency (%)
508075011
< 0.1%
508061191
< 0.1%
508005751
< 0.1%
507701211
< 0.1%
507700881
< 0.1%
507700301
< 0.1%
507673251
< 0.1%
507652651
< 0.1%
507626361
< 0.1%
507602301
< 0.1%

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct16347
Distinct (%)97.9%
Missing31
Missing (%)0.2%
Memory size1.5 MiB
Amsterdam
 
30
Residences | 2-Bedrooms | Serviced Apartment
 
16
Lovely apartment near Vondelpark
 
6
Spacious apartment near Vondelpark
 
6
Cosy apartment in the city centre of Amsterdam
 
5
Other values (16342)
16630 

Length

Max length124
Median length40
Mean length38.83687773
Min length1

Characters and Unicode

Total characters648304
Distinct characters183
Distinct categories19 ?
Distinct scripts8 ?
Distinct blocks17 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16112 ?
Unique (%)96.5%

Sample

1st rowQuiet Garden View Room & Super Fast WiFi
2nd rowStudio with private bathroom in the centre 1
3rd rowLovely, sunny 1 bed apt in Ctr (w.lift) & firepl.
4th rowRomantic, stylish B&B houseboat in canal district
5th rowComfortable double room

Common Values

ValueCountFrequency (%)
Amsterdam30
 
0.2%
Residences | 2-Bedrooms | Serviced Apartment16
 
0.1%
Lovely apartment near Vondelpark6
 
< 0.1%
Spacious apartment near Vondelpark6
 
< 0.1%
Cosy apartment in the city centre of Amsterdam5
 
< 0.1%
Amsterdam Appartement5
 
< 0.1%
Cosy apartment in Amsterdam5
 
< 0.1%
Spacious apartment in Amsterdam5
 
< 0.1%
Apartment in Amsterdam5
 
< 0.1%
Spacious apartment with garden5
 
< 0.1%
Other values (16337)16605
99.3%
(Missing)31
 
0.2%

Length

2021-08-02T23:18:16.896420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
apartment6517
 
6.4%
in5681
 
5.6%
amsterdam3915
 
3.8%
3205
 
3.2%
with2680
 
2.6%
the2194
 
2.2%
spacious2039
 
2.0%
city1726
 
1.7%
room1598
 
1.6%
and1597
 
1.6%
Other values (4902)70562
69.4%

Most occurring characters

ValueCountFrequency (%)
85321
13.2%
e57245
 
8.8%
t53063
 
8.2%
a49614
 
7.7%
r41409
 
6.4%
n37577
 
5.8%
o34670
 
5.3%
i32259
 
5.0%
m26031
 
4.0%
s21075
 
3.3%
Other values (173)210040
32.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter493372
76.1%
Space Separator85336
 
13.2%
Uppercase Letter49899
 
7.7%
Other Punctuation9695
 
1.5%
Decimal Number5295
 
0.8%
Dash Punctuation1838
 
0.3%
Math Symbol1098
 
0.2%
Close Punctuation653
 
0.1%
Open Punctuation634
 
0.1%
Other Symbol295
 
< 0.1%
Other values (9)189
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A8494
17.0%
C5755
 
11.5%
S4278
 
8.6%
B3040
 
6.1%
L2884
 
5.8%
P2621
 
5.3%
R2474
 
5.0%
E1984
 
4.0%
T1875
 
3.8%
N1841
 
3.7%
Other values (28)14653
29.4%
Lowercase Letter
ValueCountFrequency (%)
e57245
11.6%
t53063
10.8%
a49614
10.1%
r41409
 
8.4%
n37577
 
7.6%
o34670
 
7.0%
i32259
 
6.5%
m26031
 
5.3%
s21075
 
4.3%
p18368
 
3.7%
Other values (22)122061
24.7%
Other Symbol
ValueCountFrequency (%)
213
72.2%
17
 
5.8%
10
 
3.4%
10
 
3.4%
8
 
2.7%
3
 
1.0%
🌳3
 
1.0%
3
 
1.0%
2
 
0.7%
°2
 
0.7%
Other values (18)24
 
8.1%
Other Letter
ValueCountFrequency (%)
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
م1
 
3.3%
ب1
 
3.3%
س1
 
3.3%
و1
 
3.3%
ط1
 
3.3%
1
 
3.3%
Other values (16)16
53.3%
Other Punctuation
ValueCountFrequency (%)
,2653
27.4%
!1939
20.0%
&1578
16.3%
.1191
12.3%
'763
 
7.9%
/586
 
6.0%
@292
 
3.0%
"234
 
2.4%
:225
 
2.3%
*155
 
1.6%
Other values (7)79
 
0.8%
Decimal Number
ValueCountFrequency (%)
21786
33.7%
1927
17.5%
0799
15.1%
5469
 
8.9%
4398
 
7.5%
3392
 
7.4%
6135
 
2.5%
8133
 
2.5%
7130
 
2.5%
9126
 
2.4%
Math Symbol
ValueCountFrequency (%)
|550
50.1%
+514
46.8%
<13
 
1.2%
>9
 
0.8%
~8
 
0.7%
=1
 
0.1%
1
 
0.1%
1
 
0.1%
÷1
 
0.1%
Space Separator
ValueCountFrequency (%)
85321
> 99.9%
 14
 
< 0.1%
 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-1830
99.6%
5
 
0.3%
3
 
0.2%
Open Punctuation
ValueCountFrequency (%)
(630
99.4%
[4
 
0.6%
Close Punctuation
ValueCountFrequency (%)
)649
99.4%
]4
 
0.6%
Nonspacing Mark
ValueCountFrequency (%)
17
94.4%
1
 
5.6%
Initial Punctuation
ValueCountFrequency (%)
18
54.5%
15
45.5%
Final Punctuation
ValueCountFrequency (%)
52
78.8%
14
 
21.2%
Currency Symbol
ValueCountFrequency (%)
$1
50.0%
1
50.0%
Modifier Symbol
ValueCountFrequency (%)
`3
75.0%
^1
 
25.0%
Other Number
ValueCountFrequency (%)
²18
100.0%
Connector Punctuation
ValueCountFrequency (%)
_3
100.0%
Control
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin543253
83.8%
Common104985
 
16.2%
Inherited18
 
< 0.1%
Cyrillic18
 
< 0.1%
Han16
 
< 0.1%
Katakana7
 
< 0.1%
Arabic5
 
< 0.1%
Hiragana2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
85321
81.3%
,2653
 
2.5%
!1939
 
1.8%
-1830
 
1.7%
21786
 
1.7%
&1578
 
1.5%
.1191
 
1.1%
1927
 
0.9%
0799
 
0.8%
'763
 
0.7%
Other values (75)6198
 
5.9%
Latin
ValueCountFrequency (%)
e57245
 
10.5%
t53063
 
9.8%
a49614
 
9.1%
r41409
 
7.6%
n37577
 
6.9%
o34670
 
6.4%
i32259
 
5.9%
m26031
 
4.8%
s21075
 
3.9%
p18368
 
3.4%
Other values (48)171942
31.7%
Han
ValueCountFrequency (%)
2
12.5%
2
12.5%
2
12.5%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
Other values (3)3
18.8%
Cyrillic
ValueCountFrequency (%)
Е4
22.2%
А2
11.1%
М2
11.1%
О2
11.1%
м1
 
5.6%
Н1
 
5.6%
З1
 
5.6%
Б1
 
5.6%
Ы1
 
5.6%
В1
 
5.6%
Other values (2)2
11.1%
Katakana
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Arabic
ValueCountFrequency (%)
م1
20.0%
ب1
20.0%
س1
20.0%
و1
20.0%
ط1
20.0%
Inherited
ValueCountFrequency (%)
17
94.4%
1
 
5.6%
Hiragana
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII647744
99.9%
Misc Symbols250
 
< 0.1%
Punctuation113
 
< 0.1%
Latin 1 Sup84
 
< 0.1%
None21
 
< 0.1%
VS18
 
< 0.1%
Cyrillic18
 
< 0.1%
Dingbats17
 
< 0.1%
CJK16
 
< 0.1%
Katakana7
 
< 0.1%
Other values (7)16
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
85321
13.2%
e57245
 
8.8%
t53063
 
8.2%
a49614
 
7.7%
r41409
 
6.4%
n37577
 
5.8%
o34670
 
5.4%
i32259
 
5.0%
m26031
 
4.0%
s21075
 
3.3%
Other values (83)209480
32.3%
Misc Symbols
ValueCountFrequency (%)
213
85.2%
17
 
6.8%
10
 
4.0%
3
 
1.2%
2
 
0.8%
2
 
0.8%
2
 
0.8%
1
 
0.4%
Latin 1 Sup
ValueCountFrequency (%)
é29
34.5%
²18
21.4%
 14
16.7%
·8
 
9.5%
à3
 
3.6%
É3
 
3.6%
°2
 
2.4%
ó2
 
2.4%
ü2
 
2.4%
á1
 
1.2%
Other values (2)2
 
2.4%
Dingbats
ValueCountFrequency (%)
10
58.8%
2
 
11.8%
2
 
11.8%
1
 
5.9%
1
 
5.9%
1
 
5.9%
VS
ValueCountFrequency (%)
17
94.4%
1
 
5.6%
Punctuation
ValueCountFrequency (%)
52
46.0%
18
 
15.9%
15
 
13.3%
14
 
12.4%
6
 
5.3%
5
 
4.4%
3
 
2.7%
None
ValueCountFrequency (%)
8
38.1%
🌳3
 
14.3%
🏡2
 
9.5%
🌱1
 
4.8%
🌟1
 
4.8%
💕1
 
4.8%
 1
 
4.8%
🍀1
 
4.8%
🗝1
 
4.8%
💙1
 
4.8%
Arabic
ValueCountFrequency (%)
م1
20.0%
ب1
20.0%
س1
20.0%
و1
20.0%
ط1
20.0%
Misc Technical
ValueCountFrequency (%)
2
66.7%
1
33.3%
Geometric Shapes
ValueCountFrequency (%)
3
100.0%
Arrows
ValueCountFrequency (%)
1
100.0%
Currency Symbols
ValueCountFrequency (%)
1
100.0%
Math Operators
ValueCountFrequency (%)
1
100.0%
Cyrillic
ValueCountFrequency (%)
Е4
22.2%
А2
11.1%
М2
11.1%
О2
11.1%
м1
 
5.6%
Н1
 
5.6%
З1
 
5.6%
Б1
 
5.6%
Ы1
 
5.6%
В1
 
5.6%
Other values (2)2
11.1%
CJK
ValueCountFrequency (%)
2
12.5%
2
12.5%
2
12.5%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
Other values (3)3
18.8%
Hiragana
ValueCountFrequency (%)
1
50.0%
1
50.0%
Katakana
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

host_id
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14654
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69084135.44
Minimum3159
Maximum410277680
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size130.8 KiB
2021-08-02T23:18:17.051403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3159
5-th percentile2313043.15
Q19658875.5
median29172584
Q389079912.25
95-th percentile278726025.1
Maximum410277680
Range410274521
Interquartile range (IQR)79421036.75

Descriptive statistics

Standard deviation89489057.11
Coefficient of variation (CV)1.29536335
Kurtosis2.647025894
Mean69084135.44
Median Absolute Deviation (MAD)23837373.5
Skewness1.81551279
Sum1.155363081 × 1012
Variance8.008291342 × 1015
MonotonicityNot monotonic
2021-08-02T23:18:17.188072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2180015050
 
0.3%
1487406131
 
0.2%
1457453321
 
0.1%
17818787321
 
0.1%
11397756420
 
0.1%
20373185220
 
0.1%
6700541016
 
0.1%
24164410115
 
0.1%
2116788214
 
0.1%
32521392414
 
0.1%
Other values (14644)16502
98.7%
ValueCountFrequency (%)
31591
< 0.1%
35921
< 0.1%
79241
< 0.1%
120851
< 0.1%
475171
< 0.1%
498511
< 0.1%
561421
< 0.1%
579201
< 0.1%
584581
< 0.1%
594842
< 0.1%
ValueCountFrequency (%)
4102776801
 
< 0.1%
4088980896
< 0.1%
4084807891
 
< 0.1%
4081930641
 
< 0.1%
4081219172
 
< 0.1%
4080827601
 
< 0.1%
4078477401
 
< 0.1%
4076190581
 
< 0.1%
4076018111
 
< 0.1%
4074727352
 
< 0.1%

host_name
Categorical

HIGH CARDINALITY

Distinct5290
Distinct (%)31.7%
Missing59
Missing (%)0.4%
Memory size1.0 MiB
Peter
 
79
Jasper
 
78
Sophie
 
72
Jeroen
 
72
Martijn
 
72
Other values (5285)
16292 

Length

Max length35
Median length6
Mean length6.371557156
Min length1

Characters and Unicode

Total characters106182
Distinct characters95
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3279 ?
Unique (%)19.7%

Sample

1st rowDaniel
2nd rowAlexander
3rd rowJoan
4th rowFlip
5th rowEdwin

Common Values

ValueCountFrequency (%)
Peter79
 
0.5%
Jasper78
 
0.5%
Sophie72
 
0.4%
Jeroen72
 
0.4%
Martijn72
 
0.4%
Thomas71
 
0.4%
Eva69
 
0.4%
Anne69
 
0.4%
Marieke66
 
0.4%
Joost66
 
0.4%
Other values (5280)15951
95.4%

Length

2021-08-02T23:18:17.490299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
567
 
3.0%
hotel150
 
0.8%
and128
 
0.7%
jan101
 
0.5%
peter93
 
0.5%
eva88
 
0.5%
jasper84
 
0.4%
en83
 
0.4%
jeroen83
 
0.4%
anne81
 
0.4%
Other values (4857)17587
92.3%

Most occurring characters

ValueCountFrequency (%)
e12191
 
11.5%
a11355
 
10.7%
i8784
 
8.3%
n8699
 
8.2%
r6915
 
6.5%
o5043
 
4.7%
l4971
 
4.7%
t4181
 
3.9%
s3671
 
3.5%
2389
 
2.2%
Other values (85)37983
35.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter83878
79.0%
Uppercase Letter18938
 
17.8%
Space Separator2389
 
2.2%
Other Punctuation744
 
0.7%
Dash Punctuation138
 
0.1%
Decimal Number29
 
< 0.1%
Open Punctuation25
 
< 0.1%
Close Punctuation25
 
< 0.1%
Math Symbol8
 
< 0.1%
Other Symbol4
 
< 0.1%
Other values (2)4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e12191
14.5%
a11355
13.5%
i8784
10.5%
n8699
10.4%
r6915
8.2%
o5043
 
6.0%
l4971
 
5.9%
t4181
 
5.0%
s3671
 
4.4%
u2202
 
2.6%
Other values (37)15866
18.9%
Uppercase Letter
ValueCountFrequency (%)
M2360
 
12.5%
J1740
 
9.2%
A1595
 
8.4%
S1506
 
8.0%
R1135
 
6.0%
E1099
 
5.8%
L1037
 
5.5%
C881
 
4.7%
T792
 
4.2%
B787
 
4.2%
Other values (19)6006
31.7%
Other Punctuation
ValueCountFrequency (%)
&613
82.4%
.83
 
11.2%
,21
 
2.8%
'14
 
1.9%
/13
 
1.7%
Decimal Number
ValueCountFrequency (%)
112
41.4%
37
24.1%
06
20.7%
53
 
10.3%
21
 
3.4%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
2389
100.0%
Math Symbol
ValueCountFrequency (%)
+8
100.0%
Open Punctuation
ValueCountFrequency (%)
(25
100.0%
Close Punctuation
ValueCountFrequency (%)
)25
100.0%
Dash Punctuation
ValueCountFrequency (%)
-138
100.0%
Connector Punctuation
ValueCountFrequency (%)
_2
100.0%
Other Symbol
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin102816
96.8%
Common3364
 
3.2%
Han2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e12191
 
11.9%
a11355
 
11.0%
i8784
 
8.5%
n8699
 
8.5%
r6915
 
6.7%
o5043
 
4.9%
l4971
 
4.8%
t4181
 
4.1%
s3671
 
3.6%
M2360
 
2.3%
Other values (66)34646
33.7%
Common
ValueCountFrequency (%)
2389
71.0%
&613
 
18.2%
-138
 
4.1%
.83
 
2.5%
(25
 
0.7%
)25
 
0.7%
,21
 
0.6%
'14
 
0.4%
/13
 
0.4%
112
 
0.4%
Other values (7)31
 
0.9%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII105985
99.8%
Latin 1 Sup183
 
0.2%
Latin Ext A8
 
< 0.1%
Misc Symbols4
 
< 0.1%
CJK2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e12191
 
11.5%
a11355
 
10.7%
i8784
 
8.3%
n8699
 
8.2%
r6915
 
6.5%
o5043
 
4.8%
l4971
 
4.7%
t4181
 
3.9%
s3671
 
3.5%
2389
 
2.3%
Other values (58)37786
35.7%
Latin 1 Sup
ValueCountFrequency (%)
é86
47.0%
ë31
 
16.9%
è15
 
8.2%
á9
 
4.9%
í9
 
4.9%
ï6
 
3.3%
ç6
 
3.3%
ö4
 
2.2%
ü3
 
1.6%
ó3
 
1.6%
Other values (11)11
 
6.0%
Latin Ext A
ValueCountFrequency (%)
ı5
62.5%
ė2
 
25.0%
ă1
 
12.5%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%
Misc Symbols
ValueCountFrequency (%)
4
100.0%

neighbourhood_group
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing16724
Missing (%)100.0%
Memory size130.8 KiB

neighbourhood
Categorical

HIGH CORRELATION

Distinct22
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
De Baarsjes - Oud-West
2791 
De Pijp - Rivierenbuurt
2052 
Centrum-West
1827 
Centrum-Oost
1409 
Zuid
1252 
Other values (17)
7393 

Length

Max length38
Median length12
Mean length15.83765845
Min length4

Characters and Unicode

Total characters264869
Distinct characters41
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOostelijk Havengebied - Indische Buurt
2nd rowCentrum-Oost
3rd rowCentrum-West
4th rowCentrum-West
5th rowCentrum-West

Common Values

ValueCountFrequency (%)
De Baarsjes - Oud-West2791
16.7%
De Pijp - Rivierenbuurt2052
12.3%
Centrum-West1827
10.9%
Centrum-Oost1409
8.4%
Zuid1252
7.5%
Westerpark1238
7.4%
Oud-Oost1090
 
6.5%
Bos en Lommer951
 
5.7%
Oostelijk Havengebied - Indische Buurt772
 
4.6%
Oud-Noord532
 
3.2%
Other values (12)2810
16.8%

Length

2021-08-02T23:18:17.743945image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6681
17.3%
de4967
12.9%
baarsjes2791
 
7.2%
oud-west2791
 
7.2%
pijp2052
 
5.3%
rivierenbuurt2052
 
5.3%
centrum-west1827
 
4.7%
centrum-oost1409
 
3.6%
zuid1252
 
3.2%
westerpark1238
 
3.2%
Other values (27)11563
29.9%

Most occurring characters

ValueCountFrequency (%)
e34539
 
13.0%
21899
 
8.3%
r20256
 
7.6%
s18047
 
6.8%
t17949
 
6.8%
u16222
 
6.1%
-15093
 
5.7%
i10993
 
4.2%
a10781
 
4.1%
d9708
 
3.7%
Other values (31)89382
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter188070
71.0%
Uppercase Letter39807
 
15.0%
Space Separator21899
 
8.3%
Dash Punctuation15093
 
5.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e34539
18.4%
r20256
10.8%
s18047
9.6%
t17949
9.5%
u16222
8.6%
i10993
 
5.8%
a10781
 
5.7%
d9708
 
5.2%
n8945
 
4.8%
o8642
 
4.6%
Other values (13)31988
17.0%
Uppercase Letter
ValueCountFrequency (%)
O8131
20.4%
W6691
16.8%
D5080
12.8%
B4922
12.4%
C3332
8.4%
P2052
 
5.2%
R2052
 
5.2%
Z1876
 
4.7%
N1231
 
3.1%
I1176
 
3.0%
Other values (6)3264
8.2%
Space Separator
ValueCountFrequency (%)
21899
100.0%
Dash Punctuation
ValueCountFrequency (%)
-15093
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin227877
86.0%
Common36992
 
14.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e34539
15.2%
r20256
 
8.9%
s18047
 
7.9%
t17949
 
7.9%
u16222
 
7.1%
i10993
 
4.8%
a10781
 
4.7%
d9708
 
4.3%
n8945
 
3.9%
o8642
 
3.8%
Other values (29)71795
31.5%
Common
ValueCountFrequency (%)
21899
59.2%
-15093
40.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII264869
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e34539
 
13.0%
21899
 
8.3%
r20256
 
7.6%
s18047
 
6.8%
t17949
 
6.8%
u16222
 
6.1%
-15093
 
5.7%
i10993
 
4.2%
a10781
 
4.1%
d9708
 
3.7%
Other values (31)89382
33.7%

latitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct5829
Distinct (%)34.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.36545188
Minimum52.27882
Maximum52.42804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size130.8 KiB
2021-08-02T23:18:17.866488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum52.27882
5-th percentile52.3423915
Q152.3551
median52.36483
Q352.37534
95-th percentile52.3914155
Maximum52.42804
Range0.14922
Interquartile range (IQR)0.02024

Descriptive statistics

Standard deviation0.01660735033
Coefficient of variation (CV)0.0003171432639
Kurtosis2.146954534
Mean52.36545188
Median Absolute Deviation (MAD)0.01003
Skewness-0.1492107436
Sum875759.8173
Variance0.0002758040848
MonotonicityNot monotonic
2021-08-02T23:18:18.006278image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.3742623
 
0.1%
52.364316
 
0.1%
52.3739215
 
0.1%
52.3534514
 
0.1%
52.356613
 
0.1%
52.388112
 
0.1%
52.3654112
 
0.1%
52.3721112
 
0.1%
52.3619911
 
0.1%
52.3581311
 
0.1%
Other values (5819)16585
99.2%
ValueCountFrequency (%)
52.278821
< 0.1%
52.290341
< 0.1%
52.29091
< 0.1%
52.291221
< 0.1%
52.291251
< 0.1%
52.291321
< 0.1%
52.291451
< 0.1%
52.291581
< 0.1%
52.291651
< 0.1%
52.29171
< 0.1%
ValueCountFrequency (%)
52.428041
< 0.1%
52.425341
< 0.1%
52.425121
< 0.1%
52.424821
< 0.1%
52.424761
< 0.1%
52.424671
< 0.1%
52.424471
< 0.1%
52.423971
< 0.1%
52.423951
< 0.1%
52.423791
< 0.1%

longitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct9116
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.889622679
Minimum4.75571
Maximum5.065527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size130.8 KiB
2021-08-02T23:18:18.150607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4.75571
5-th percentile4.8438275
Q14.863935
median4.88704
Q34.90941
95-th percentile4.948211
Maximum5.065527
Range0.309817
Interquartile range (IQR)0.045475

Descriptive statistics

Standard deviation0.03624438126
Coefficient of variation (CV)0.00741251087
Kurtosis1.100458643
Mean4.889622679
Median Absolute Deviation (MAD)0.02277
Skewness0.5387269765
Sum81774.04969
Variance0.001313655173
MonotonicityNot monotonic
2021-08-02T23:18:18.287975image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8992618
 
0.1%
4.9143814
 
0.1%
4.9022114
 
0.1%
4.8963513
 
0.1%
4.8886710
 
0.1%
4.891739
 
0.1%
4.872518
 
< 0.1%
4.910338
 
< 0.1%
4.876678
 
< 0.1%
4.888578
 
< 0.1%
Other values (9106)16614
99.3%
ValueCountFrequency (%)
4.755711
< 0.1%
4.757241
< 0.1%
4.762871
< 0.1%
4.769611
< 0.1%
4.769951
< 0.1%
4.770031
< 0.1%
4.771391
< 0.1%
4.771531
< 0.1%
4.773461
< 0.1%
4.775071
< 0.1%
ValueCountFrequency (%)
5.0655271
< 0.1%
5.027991
< 0.1%
5.026431
< 0.1%
5.018391
< 0.1%
5.018131
< 0.1%
5.016851
< 0.1%
5.016671
< 0.1%
5.015781
< 0.1%
5.014781
< 0.1%
5.014711
< 0.1%

room_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
Entire home/apt
12992 
Private room
3569 
Hotel room
 
116
Shared room
 
47

Length

Max length15
Median length15
Mean length14.31386032
Min length10

Characters and Unicode

Total characters239385
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate room
2nd rowPrivate room
3rd rowEntire home/apt
4th rowPrivate room
5th rowPrivate room

Common Values

ValueCountFrequency (%)
Entire home/apt12992
77.7%
Private room3569
 
21.3%
Hotel room116
 
0.7%
Shared room47
 
0.3%

Length

2021-08-02T23:18:18.647468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-02T23:18:18.719647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
entire12992
38.8%
home/apt12992
38.8%
room3732
 
11.2%
private3569
 
10.7%
hotel116
 
0.3%
shared47
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e29716
12.4%
t29669
12.4%
o20572
8.6%
r20340
8.5%
16724
 
7.0%
m16724
 
7.0%
a16608
 
6.9%
i16561
 
6.9%
h13039
 
5.4%
E12992
 
5.4%
Other values (9)46440
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter192945
80.6%
Uppercase Letter16724
 
7.0%
Space Separator16724
 
7.0%
Other Punctuation12992
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e29716
15.4%
t29669
15.4%
o20572
10.7%
r20340
10.5%
m16724
8.7%
a16608
8.6%
i16561
8.6%
h13039
6.8%
n12992
6.7%
p12992
6.7%
Other values (3)3732
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
E12992
77.7%
P3569
 
21.3%
H116
 
0.7%
S47
 
0.3%
Space Separator
ValueCountFrequency (%)
16724
100.0%
Other Punctuation
ValueCountFrequency (%)
/12992
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin209669
87.6%
Common29716
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e29716
14.2%
t29669
14.2%
o20572
9.8%
r20340
9.7%
m16724
8.0%
a16608
7.9%
i16561
7.9%
h13039
6.2%
E12992
6.2%
n12992
6.2%
Other values (7)20456
9.8%
Common
ValueCountFrequency (%)
16724
56.3%
/12992
43.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII239385
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e29716
12.4%
t29669
12.4%
o20572
8.6%
r20340
8.5%
16724
 
7.0%
m16724
 
7.0%
a16608
 
6.9%
i16561
 
6.9%
h13039
 
5.4%
E12992
 
5.4%
Other values (9)46440
19.4%

price
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct509
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.1597704
Minimum0
Maximum8000
Zeros16
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size130.8 KiB
2021-08-02T23:18:18.821021image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile57
Q195
median129
Q3180
95-th percentile320
Maximum8000
Range8000
Interquartile range (IQR)85

Descriptive statistics

Standard deviation172.4871696
Coefficient of variation (CV)1.104555732
Kurtosis1033.124837
Mean156.1597704
Median Absolute Deviation (MAD)40
Skewness25.77570208
Sum2611616
Variance29751.82366
MonotonicityNot monotonic
2021-08-02T23:18:18.961074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150881
 
5.3%
100737
 
4.4%
120617
 
3.7%
200535
 
3.2%
125479
 
2.9%
110414
 
2.5%
90386
 
2.3%
250386
 
2.3%
80376
 
2.2%
130357
 
2.1%
Other values (499)11556
69.1%
ValueCountFrequency (%)
016
0.1%
41
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
161
 
< 0.1%
172
 
< 0.1%
181
 
< 0.1%
192
 
< 0.1%
208
< 0.1%
213
 
< 0.1%
ValueCountFrequency (%)
80002
< 0.1%
79991
 
< 0.1%
64772
< 0.1%
50001
 
< 0.1%
30001
 
< 0.1%
25002
< 0.1%
20003
< 0.1%
19251
 
< 0.1%
17501
 
< 0.1%
15111
 
< 0.1%

minimum_nights
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct63
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.009208323
Minimum1
Maximum1100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size130.8 KiB
2021-08-02T23:18:19.107107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile7
Maximum1100
Range1099
Interquartile range (IQR)1

Descriptive statistics

Standard deviation19.76198833
Coefficient of variation (CV)4.92914978
Kurtosis1802.352945
Mean4.009208323
Median Absolute Deviation (MAD)1
Skewness37.94287912
Sum67050
Variance390.5361826
MonotonicityNot monotonic
2021-08-02T23:18:19.244470image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26207
37.1%
34027
24.1%
12783
16.6%
41403
 
8.4%
5928
 
5.5%
7517
 
3.1%
6235
 
1.4%
14112
 
0.7%
10104
 
0.6%
3076
 
0.5%
Other values (53)332
 
2.0%
ValueCountFrequency (%)
12783
16.6%
26207
37.1%
34027
24.1%
41403
 
8.4%
5928
 
5.5%
6235
 
1.4%
7517
 
3.1%
835
 
0.2%
913
 
0.1%
10104
 
0.6%
ValueCountFrequency (%)
11001
 
< 0.1%
10011
 
< 0.1%
10001
 
< 0.1%
9991
 
< 0.1%
5001
 
< 0.1%
3656
< 0.1%
3003
< 0.1%
2401
 
< 0.1%
2221
 
< 0.1%
2003
< 0.1%

number_of_reviews
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct409
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.10368333
Minimum0
Maximum865
Zeros2318
Zeros (%)13.9%
Negative0
Negative (%)0.0%
Memory size130.8 KiB
2021-08-02T23:18:19.387248image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q321
95-th percentile101
Maximum865
Range865
Interquartile range (IQR)19

Descriptive statistics

Standard deviation55.37988933
Coefficient of variation (CV)2.297569569
Kurtosis43.8654417
Mean24.10368333
Median Absolute Deviation (MAD)7
Skewness5.67167682
Sum403110
Variance3066.932142
MonotonicityNot monotonic
2021-08-02T23:18:19.520032image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02318
 
13.9%
11376
 
8.2%
21058
 
6.3%
3920
 
5.5%
4769
 
4.6%
5660
 
3.9%
6620
 
3.7%
7555
 
3.3%
8493
 
2.9%
10486
 
2.9%
Other values (399)7469
44.7%
ValueCountFrequency (%)
02318
13.9%
11376
8.2%
21058
6.3%
3920
 
5.5%
4769
 
4.6%
5660
 
3.9%
6620
 
3.7%
7555
 
3.3%
8493
 
2.9%
9430
 
2.6%
ValueCountFrequency (%)
8651
< 0.1%
8381
< 0.1%
7901
< 0.1%
7781
< 0.1%
7131
< 0.1%
7111
< 0.1%
7041
< 0.1%
6961
< 0.1%
6831
< 0.1%
6221
< 0.1%

last_review
Categorical

HIGH CARDINALITY
MISSING

Distinct2040
Distinct (%)14.2%
Missing2318
Missing (%)13.9%
Memory size1015.1 KiB
2020-01-02
 
118
2019-10-20
 
90
2020-01-03
 
85
2020-03-08
 
85
2020-02-16
 
84
Other values (2035)
13944 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters144060
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique400 ?
Unique (%)2.8%

Sample

1st row2019-11-21
2nd row2020-03-27
3rd row2020-01-02
4th row2020-07-25
5th row2020-02-06

Common Values

ValueCountFrequency (%)
2020-01-02118
 
0.7%
2019-10-2090
 
0.5%
2020-01-0385
 
0.5%
2020-03-0885
 
0.5%
2020-02-1684
 
0.5%
2019-10-2177
 
0.5%
2020-01-0177
 
0.5%
2019-09-1568
 
0.4%
2019-09-1662
 
0.4%
2020-02-1752
 
0.3%
Other values (2030)13608
81.4%
(Missing)2318
 
13.9%

Length

2021-08-02T23:18:19.790800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-01-02118
 
0.8%
2019-10-2090
 
0.6%
2020-01-0385
 
0.6%
2020-03-0885
 
0.6%
2020-02-1684
 
0.6%
2020-01-0177
 
0.5%
2019-10-2177
 
0.5%
2019-09-1568
 
0.5%
2019-09-1662
 
0.4%
2019-12-3052
 
0.4%
Other values (2030)13608
94.5%

Most occurring characters

ValueCountFrequency (%)
035638
24.7%
-28812
20.0%
225519
17.7%
123245
16.1%
97957
 
5.5%
85716
 
4.0%
74889
 
3.4%
63904
 
2.7%
33263
 
2.3%
52832
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number115248
80.0%
Dash Punctuation28812
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
035638
30.9%
225519
22.1%
123245
20.2%
97957
 
6.9%
85716
 
5.0%
74889
 
4.2%
63904
 
3.4%
33263
 
2.8%
52832
 
2.5%
42285
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
-28812
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common144060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
035638
24.7%
-28812
20.0%
225519
17.7%
123245
16.1%
97957
 
5.5%
85716
 
4.0%
74889
 
3.4%
63904
 
2.7%
33263
 
2.3%
52832
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII144060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
035638
24.7%
-28812
20.0%
225519
17.7%
123245
16.1%
97957
 
5.5%
85716
 
4.0%
74889
 
3.4%
63904
 
2.7%
33263
 
2.3%
52832
 
2.0%

reviews_per_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct596
Distinct (%)4.1%
Missing2318
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean0.6094911842
Minimum0.01
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size130.8 KiB
2021-08-02T23:18:19.905576image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.03
Q10.11
median0.27
Q30.6
95-th percentile2.69
Maximum30
Range29.99
Interquartile range (IQR)0.49

Descriptive statistics

Standard deviation1.122150628
Coefficient of variation (CV)1.841126922
Kurtosis119.2612226
Mean0.6094911842
Median Absolute Deviation (MAD)0.19
Skewness7.423858119
Sum8780.33
Variance1.259222032
MonotonicityNot monotonic
2021-08-02T23:18:20.038638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02442
 
2.6%
0.05435
 
2.6%
0.03397
 
2.4%
0.04378
 
2.3%
0.06359
 
2.1%
0.09340
 
2.0%
0.08333
 
2.0%
0.1318
 
1.9%
0.11304
 
1.8%
0.13297
 
1.8%
Other values (586)10803
64.6%
(Missing)2318
 
13.9%
ValueCountFrequency (%)
0.01133
 
0.8%
0.02442
2.6%
0.03397
2.4%
0.04378
2.3%
0.05435
2.6%
0.06359
2.1%
0.07270
1.6%
0.08333
2.0%
0.09340
2.0%
0.1318
1.9%
ValueCountFrequency (%)
301
< 0.1%
29.021
< 0.1%
28.311
< 0.1%
211
< 0.1%
18.581
< 0.1%
17.481
< 0.1%
161
< 0.1%
14.791
< 0.1%
14.711
< 0.1%
131
< 0.1%

calculated_host_listings_count
Real number (ℝ≥0)

HIGH CORRELATION

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.801124133
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size130.8 KiB
2021-08-02T23:18:20.155185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile5
Maximum50
Range49
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.611364083
Coefficient of variation (CV)2.005061182
Kurtosis104.5765644
Mean1.801124133
Median Absolute Deviation (MAD)0
Skewness9.189609196
Sum30122
Variance13.04195054
MonotonicityNot monotonic
2021-08-02T23:18:20.267371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
113507
80.8%
21696
 
10.1%
3444
 
2.7%
4176
 
1.1%
6162
 
1.0%
5135
 
0.8%
784
 
0.5%
880
 
0.5%
963
 
0.4%
5050
 
0.3%
Other values (10)327
 
2.0%
ValueCountFrequency (%)
113507
80.8%
21696
 
10.1%
3444
 
2.7%
4176
 
1.1%
5135
 
0.8%
6162
 
1.0%
784
 
0.5%
880
 
0.5%
963
 
0.4%
1050
 
0.3%
ValueCountFrequency (%)
5050
0.3%
3131
0.2%
2142
0.3%
2040
0.2%
1616
 
0.1%
1515
 
0.1%
1428
0.2%
1313
 
0.1%
1248
0.3%
1144
0.3%

availability_365
Real number (ℝ≥0)

ZEROS

Distinct363
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.5230806
Minimum0
Maximum365
Zeros10763
Zeros (%)64.4%
Negative0
Negative (%)0.0%
Memory size130.8 KiB
2021-08-02T23:18:20.514867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q371
95-th percentile354
Maximum365
Range365
Interquartile range (IQR)71

Descriptive statistics

Standard deviation111.861502
Coefficient of variation (CV)1.848245346
Kurtosis1.627264063
Mean60.5230806
Median Absolute Deviation (MAD)0
Skewness1.755180469
Sum1012188
Variance12512.99564
MonotonicityNot monotonic
2021-08-02T23:18:20.649872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010763
64.4%
365272
 
1.6%
364173
 
1.0%
89131
 
0.8%
90115
 
0.7%
8897
 
0.6%
285
 
0.5%
36384
 
0.5%
18076
 
0.5%
18576
 
0.5%
Other values (353)4852
29.0%
ValueCountFrequency (%)
010763
64.4%
175
 
0.4%
285
 
0.5%
351
 
0.3%
442
 
0.3%
544
 
0.3%
643
 
0.3%
751
 
0.3%
844
 
0.3%
935
 
0.2%
ValueCountFrequency (%)
365272
1.6%
364173
1.0%
36384
 
0.5%
36264
 
0.4%
36135
 
0.2%
36041
 
0.2%
35928
 
0.2%
35858
 
0.3%
35723
 
0.1%
35615
 
0.1%

Interactions

2021-08-02T23:18:03.286829image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:03.396652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:03.499159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:03.609432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:03.716491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:03.826432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:03.935608image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:04.038140image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:04.152823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:04.268742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:04.388873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:04.499590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:04.608479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:04.726628image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:04.840758image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:04.959771image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:05.071399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:05.180580image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:05.310184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:05.533156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:05.649177image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:05.772810image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:05.894227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:06.024651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:06.152537image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:06.284763image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:06.409971image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:06.531803image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:06.659078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:06.784860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:06.914017image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:07.032812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:07.149797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:07.276062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:07.399089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:07.526121image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:07.645768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:07.763299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:07.882383image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:08.003845image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:08.128426image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:08.367143image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:08.489187image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:08.620799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:08.748836image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:08.880567image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:09.005508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:09.128254image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:09.255399image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:09.381970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:09.512008image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:09.624820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:09.736452image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:09.857253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:09.974066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:10.094657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:10.208196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:10.320968image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:10.438099image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:10.553367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:10.672136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:10.781378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:10.889469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:11.122335image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:11.235714image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:11.353420image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:11.463909image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:11.572081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:11.684625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:11.795887image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:11.910694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:12.027752image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:12.153746image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:12.277914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:12.396683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:12.521891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:12.640481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:12.756453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:12.876074image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:12.994132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:13.110339image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:13.214194image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:13.317403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:13.428944image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:13.537418image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:13.649495image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:13.855891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:13.959445image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:14.066963image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:14.173207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:14.284246image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:14.394520image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:14.503958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:14.620581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:14.734425image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:14.851270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:14.961336image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:15.071554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:15.183928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-02T23:18:15.295683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-08-02T23:18:20.783956image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-02T23:18:20.980228image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-02T23:18:21.176747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-02T23:18:21.374222image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-02T23:18:21.538183image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-02T23:18:15.542531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-02T23:18:15.833879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-02T23:18:16.020195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-02T23:18:16.135072image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
02818Quiet Garden View Room & Super Fast WiFi3159DanielNaNOostelijk Havengebied - Indische Buurt52.364354.94358Private room6032782019-11-212.901137
120168Studio with private bathroom in the centre 159484AlexanderNaNCentrum-Oost52.364074.89393Private room10613392020-03-273.7320
225428Lovely, sunny 1 bed apt in Ctr (w.lift) & firepl.56142JoanNaNCentrum-West52.374904.88487Entire home/apt1001452020-01-020.12158
327886Romantic, stylish B&B houseboat in canal district97647FlipNaNCentrum-West52.387614.89188Private room13822212020-07-252.171217
428871Comfortable double room124245EdwinNaNCentrum-West52.367754.89092Private room7523382020-02-064.522298
529051Comfortable single room124245EdwinNaNCentrum-Oost52.365844.89111Private room5524802020-02-265.442318
641125Amsterdam Center Entire Apartment178515FatihNaNCentrum-West52.379204.88432Entire home/apt1604892020-02-100.7610
743109Oasis in the middle of Amsterdam188098AukjeNaNCentrum-West52.373664.88808Entire home/apt2113592019-04-160.561365
843980View into park / museum district (long/short stay)65041YmNaNZuid52.357354.86158Entire home/apt677612015-04-230.5320
946386Cozy loft in central Amsterdam207342JoostNaNDe Pijp - Rivierenbuurt52.351984.90746Entire home/apt150332018-01-030.0310

Last rows

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365
1671450760230Luxury appartment in the heart of the Oud-West11227766FlorentineNaNDe Baarsjes - Oud-West52.3649804.862352Entire home/apt127140NaNNaN119
1671550762636Double/Triple with private bathroom in a homestay311743899HilletjeNaNBos en Lommer52.3820994.856841Private room7620NaNNaN1336
1671650765265Private 55m2 apartment next to Rijksmuseum (500m)101998763JoostNaNZuid52.3539674.886547Entire home/apt8510NaNNaN1164
1671750767325lovely studio near center of Amsterdam410277680DavoudNaNNoord-Oost52.3992514.938890Entire home/apt9010NaNNaN1187
1671850770030Spacious room with balcony in lively neighbourhood29687232NielsNaNDe Baarsjes - Oud-West52.3648524.863416Private room5510NaNNaN176
1671950770088Van Westhouse 3 City centre Canal house studio19228755ThedieNaNCentrum-Oost52.3725364.902582Private room9950NaNNaN3256
1672050770121Sfeervol en warm appartement * A’dam Oud-West *250631796DannyNaNDe Baarsjes - Oud-West52.3720014.872452Entire home/apt6130NaNNaN1191
1672150800575leuke plek voor 2 in de pijp41488364BartNaNDe Pijp - Rivierenbuurt52.3518094.906918Entire home/apt8010NaNNaN1356
1672250806119Spacious and Light apartment in West of Amsterdam!14289831HeinNaNBos en Lommer52.3790644.858107Entire home/apt12570NaNNaN126
1672350807501Cozy house with the garden19645752AndreiNaNSlotervaart52.3671554.838638Entire home/apt12050NaNNaN124